grobotis in the Sales Force Automation Industry

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Loss of Amazon Rainforest Resilience: Author Contributions

This paper is available on under CC 4.0 license. Arxiv Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 4 Conclusion The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance in understanding the molecular basis of biological processes. The application of geometric graph theory and atom-level graph coloring techniques provides a powerful framework for analyzing biomolecules and capturing their intricate relationships. By utilizing the concept of geometric subgraphs and constructing multi-scale weighted colored geometric subgraphs (MWCGS), we can effectively represent the structural and functional properties of PPIs. The site-specific MWCGS features allow us to extract meaningful patterns and characteristics, shedding light on the effects of mutations and the underlying molecular interactions. In this work, we developed a mutation-induced binding free energy change predictor, called GGL-PPI, by incorporating site-specific MWCGS features for PPIs and gradient-boosting trees. Our method demonstrates superior performance compared to existing methods. The model was validated on three datasets: AB-Bind S645, SKEMPI 1.0 S1131, and SKEMPI 2.0 S4169 and S8338, showcasing its robustness and effectiveness. Furthermore, GGL-PPI was evaluated on a blind test set, the Ssym dataset. To prevent data leakage between the test and training sets, the model was trained on a homology-reduced balanced training set Q3488. This approach ensures the reliability and fairness of the evaluation process. GGL-PPI exhibits the most unbiased and superior performance in predicting binding free energy changes for both direct and reverse mutations, outperforming other existing methods, particularly for reverse mutations. Overall, the results highlight the potential of the GGL-PPI approach in accurately predicting mutation-induced binding free energy changes in protein-protein interactions, providing valuable insights into the molecular mechanisms underlying protein-protein interactions and facilitating drug design and discovery efforts. 5 Data and Software Availability The source code is available at Github: https://github.com/NguyenLabUKY/GGL-Mutation. 6 Competing interests No competing interest is declared. 7 Acknowledgments This work is supported in part by funds from the National Science Foundation (NSF: # 2053284, # 2151802, and # 2245903), and the University of Kentucky Startup Fund. References [1] Dana Chuderland and Rony Seger. 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PPIs and the Prediction of Mutation-Induced Binding Free Energy Changes: What It All Means

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 4. FUTURE PROSPECTS The search for transiting planets around small stars has been motivated in large part by their potential for atmospheric characterization. Owing to the size and proximity of its host, K2-315b is thus one of the few known terrestrial exoplanets possibly amenable for atmospheric characterization in the next two decades. In order to quantify and contextualize its prospects for atmospheric study, we followed the same approach as for TRAPPIST-1 in Gillon et al. (2016) (see de Wit & Seager 2013), focusing here on all known terrestrial planets. We selected terrestrial planets as planets with a reported radius below 1.6 R⊕ in the NASA Exoplanet Archive[4] (Rogers 2015; Fulton et al. 2017). We thus derive the amplitude of the planets’ signals in transmission as: where Rp is the planetary radius, R∗ is the stellar radius, and heff is the effective atmospheric height, µ is the atmospheric mean molecular mass, T is the atmospheric temperature and g is the local gravity. We assume heff to cover seven atmospheric scale heights, µ the atmospheric mean molecular mass to be 20 amu, and the atmospheric temperature to be the equilibrium temperature for a Bond albedo of 0. For the planets with missing masses, we estimated g using the model of Chen & Kipping (2017). The signal amplitudes are reported in Figure 7 together with the SNR relative to TRAPPIST-1 b’s, calculated by scaling the signal amplitude with the hosts’ brightness in J band. We find that K2-315b fares closely to the outer planets of TRAPPIST-1 in terms of potential for atmospheric exploration with JWST— its warmer and thus larger atmosphere compensating for its larger star. In fact, its relative SNR for transmission spectroscopy is half those of TRAPPIST-1 f–h, meaning that assessing the presence of a µ ∼ 20 atmosphere around the planet would require of the order of 40 transits—four times the ∼10 transits required for a similar assessment for TRAPPIST-1 f–h (Lustig-Yaeger et al. 2019). K2-315b is thus at the very edge of JWST’s capability for atmospheric characterization, mostly due to its “large” host star. The derivation above allows us to rank planets in terms of relative potential for atmospheric characterization, assuming a similar atmospheric scenario. In practice, a significant difference in atmospheric mean molecular mass, surface pressure, and/or cloud/haze altitude will strongly affect the actual potential of a planet for characterization (Lustig-Yaeger et al. 2019). For example, in some cases clouds could render the characterization of the favorable TRAPPIST-1 e difficult even with the generation of instruments following the JWST (i.e., LUVOIR, Pidhorodetska et al. 2020). With an estimated radial velocity semi-amplitude of 1.3 m s−1 (assuming a mass comparable to that of Earth), the planet could be accessible for mass measurements using modern ultra-precise radial velocity instruments. Such possibilities and a ranking amongst the 10 best-suited Earth-sized planets for atmospheric study, EPIC 249631677 b will therefore play an important role in the upcoming era of comparative exoplanetology for terrestrial worlds. It will surely be a prime target for the generation of observatories to follow JWST and bring the field fully into this new era. This project makes use of publicly available K2 data. P.N. would like to acknowledge funding for Kerr Fellowship and Elliot Fellowship at MIT. J.d.W. and MIT gratefully acknowledge financial support from the Heising-Simons Foundation, Dr. and Mrs. Colin Masson and Dr. Peter A. Gilman for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. B.V.R. thanks the Heising-Simons Foundation for support. V.V.G. is a F.R.S.-FNRS Research Associate. M.G. and E.J. are F.R.S.-FNRS Senior Research Associates. This work was also partially supported by a grant from the Simons Foundation (PI Queloz, grant number 327127), and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement Number 336480, from the ARC grant for Concerted Research Actions financed by the Wallonia-Brussels Federation, from the Balzan Prize Foundation, from F.R.S-FNRS (Research Project ID T010920F), from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement n◦ 803193/BEBOP), and from a Leverhulme Trust Research Project Grant n◦ RPG-2018-418. 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π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Future Prospects & References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 3. ANALYSIS AND VALIDATION 3.1. Stellar Host Characterization 3.1.1. Semi-empirical Stellar Parameters We constructed the spectral energy distribution (SED) of EPIC 249631677 using photometric magnitudes from Gaia (GBP and GRP ; Gaia Collaboration et al. 2018) and the AllWISE source catalog (J, H, Ks, W1, W2, and W3; Cutri et al. 2013). The corresponding fluxes for these magnitudes are tabulated on VizieR (Ochsenbein et al. 2000) and shown in Figure 5 and Table 1. The parallax of EPIC 249631677 is π = 17.61 ± 0.09 mas, which yields a distance of 56.8 ± 0.3 pc (Gaia Collaboration et al. 2018; Stassun & Torres 2018). We then derived the stellar luminosity L∗ by integrating over the SED, which yielded L∗ = 0.0041 ± 0.0001L . Two independent methods were applied to obtain stellar mass. First, we used the empirical M∗−MKs relation (applying the metallicity obtained in Section 3.1.2) from Mann et al. (2019) to obtain M∗ = 0.1721 ± 0.0044M . We also applied stellar evolution modeling, using the models presented in Fernandes et al. (2019), using as a constraint the luminosity inferred above and the metallicity derived in Sect. 3.1.2). We considered the stellar age to be >1 Gyr in the absence of signs of youth, such as presence of prominent flares (Ilin et al. 2019, see Section 3.1.3). We obtained with this method M∗ = 0.176 ± 0.004 M . This uncertainty reflects the error propagation on the stellar luminosity and metallicity, but also the uncertainty associated with the input physics of the stellar models. We combined these two mass estimates as in Van Grootel et al. (2018) to obtain M∗ = 0.174±0.004M as our best estimate for the stellar mass of EPIC 249631677. Given its proximity, we expect minimal extinction (Av) for the target; the SED fitting analysis described in Section 3.2.1 similarly constrains it to be 0.02 at 3σ confidence and we adopt that upper limit here. Finally, we note that given its luminosity, mass, and Gaia colors this star is likely to be fully convective (Jao et al. 2018; Rabus et al. 2019). Due to the absence of a strong constraint on the stellar density from the transits, we further obtained stellar radius, surface gravity, effective temperature and density from our evolutionary models. Table 1 summarizes the results from this analysis, along with other properties of the star. Our values are consistent with those listed in the TESS Input Catalog (Stassun et al. 2019), and we adopt them for the remainder of this analysis. 3.1.2. Reconaissance Spectroscopy To confirm EPIC 249631677’s stellar properties and better characterize the system, we acquired an optical spectrum using Keck/HIRES (Vogt et al. 1994) on UT 30 May 2020. The observation took place in 0.6 00 effective seeing and using the C2 decker without the HIRES iodine gas cell, giving an effective resolution of λ/∆λ ≈ 55, 000 from 3600 ˚A to 7990 ˚A. We exposed for 1800 s and obtained SNR of roughly 23 per pixel. Data reduction followed the standard approach of the California Planet Search consortium (Howard et al. 2010). We used our Keck/HIRES radial velocity and Gaia DR2 data to estimate the 3D galactic (UV W) space velocity using the online kinematics calculator[2] of Rodriguez (2016). Following Chubak et al. (2012), our Keck/HIRES spectrum gives a barycentric radial velocity of 6.25±0.17 km s−1 . With the Gaia-derived coordinates, proper motion, and distance listed in Table 1, we find (U, V, W) values of (−17.02, −9.06, +33.66) km s−1 , indicating a likely membership in the Milky Way’s thin disk (Bensby et al. 2014). Using the SpecMatch-Empirical algorithm (Yee et al. 2017), we derive from our HIRES spectrum stellar parameters of Teff = 3195 ± 70 K, R∗ = 0.23 ± 0.10R , and [Fe/H]= −0.24 ± 0.09, consistent with the values tabulated in Table 1. The three best-matching stars in the SpecMatch-Empirical template library are GJ 15B, GJ 447, and GJ 725B, which have spectral types of M3.5V, M4V, and M3.5V, respectively. Given the close match between the spectra of these stars and our target (see Fig. 4), we therefore classify EPIC 249631677 as an M dwarf with subclass 3.5±0.5. We see no evidence of emission line cores at Hα, consistent with our determination that our target is not a young star. We see no evidence of spectral broadening compared to these three stars (which all have v sin i < 2.5 km s−1 ; Reiners et al. 2012), so we set an upper limit on EPIC 249631677’s projected rotational velocity of < 5 km s−1 , comparable to the spectral resolution of HIRES. 3.1.3. Stellar Variability The long-term variations apparent in the everest light curve (Figure 1) are not evident in light curves from other reduction pipelines (e.g., K2SFF). These variations likely arise from systematics and are not reliable for estimating the stellar rotation period (Esselstein et al. 2018). Similarly, no flares are apparent either in the K2 or SPECULOOS data. Flare rates peak for ∼M3.5 stars in TESS data (G¨unther et al. 2020). However, given the long integration time of 29.4 minutes as well as a need for data processing which corrects for the saw-tooth pattern, flare signals, unless very prominent, are expected to be difficult to detect in K2 long cadence data. 3.2. Vetting In order to produce a transit depth on the level of 0.2% in the light curve of the primary target, a background eclipsing binary producing eclipses with depths of 25% to 50% would have to be 5.25 to 6.0 mag fainter than the target, respectively. Qualitatively, the odds of EPIC 249631677 hosting a planet are higher than the odds of such magnitude contrast eclipsing binary being present within the SPECULOOS aperture, given occurrence rates of M-dwarf planets (Dressing & Charbonneau 2013; Mulders et al. 2015; Hardegree-Ullman et al. 2019). A stringent quantitative constraint can be placed using the ingress/egress duration (T12/T34) compared to the total transit duration (T14) (Seager & Mall´enOrnelas 2003, Equation 21). Such a test yields an upper limit on the relative radius of the transiting body. By assuming equal effective surface temperatures, the lower magnitude limit ∆m (corresponding to a flux difference ∆F) for a blended binary mimicking a signal of depth δ is given by: Using the posterior for the transit fit (See Section 3.3), we find for EPIC 249631677 that such a background object can be fainter at most by 1.73 mag at the 3σ level. Fortunately, EPIC 249631677 has a significant proper motion, ∼140 mas yr−1 , which allows us to investigate the presence of background sources at its current sky position. We looked at archival imaging of EPIC 246331677 going back to 1953[3] . A POSS I plate from 1953 is the publicly available oldest image of EPIC 249631677, and it does not show any background source at the current position of the target as shown in Figure 6. The plate is sensitive to objects at least 3.5 magnitudes fainter than the target. Similarly, the Hubble Guide Star Catalogue (GSC), with a limiting magnitude of 20 (Lasker et al. 1990), does not show any background source. While POSS II would go the deepest in terms of limiting magnitude (20.8, Reid et al. 1991), the star has moved appreciably closer to its current location, precluding a definitive measurement from this image. Overall, using archival images we can rule out the possibility of the transit signal originating from background star at a high level of confidence. 3.2.1. Binarity of the Host Star Despite the lack of background sources, the host star could produce a false-positive transit signal if it were a grazing eclipsing binary or a hierarchical eclipsing binary. We investigated the evidence for host star binarity using the isochrones software package (Morton 2015), which performs isochrone fitting in the context of the MESA (Paxton et al. 2011, 2013, 2015) Isochrones and Stellar Tracks database (Dotter 2016; Choi et al. 2016). Single-star and binary evolutionary models are available within isochrones, and the inference is performed via the nested sampling algorithm MULTINEST (Feroz et al. 2009) (as implemented in the PyMultiNest software package (Buchner et al. 2014)), which allows for direct comparisons of the Bayesian evidence ln Z. We tested both single-star and binary models using the priors on photometric magnitudes and stellar distance described in Section 3.1.1. The inferred properties from the single-star model fit are consistent with those given in Table 1 at the 2σ level. The ln Z for the single-star model is −213.86±0.04, whereas the ln Z for the binary model is −229.6 ± 0.2. According to Kass & Raftery (1995), the corresponding Bayes factor of 16 indicates “decisive” evidence in favor of the single-star model. We also examined our Keck/HIRES spectrum for secondary lines that would indicate the presence of another star following the approach of Kolbl et al. (2015). We found no evidence of additional lines down to the method’s standard sensitivity limit of ∆V = 5 mag for ∆v > 10 km s−1 , consistent with EPIC 249631677 being a single, isolated star. We therefore conclude that the available data strongly support EPIC 246331677 being a single star. 3.2.2. Photometric Tests We performed a series of tests on the photometric data to rule out false-positive scenarios. First, we performed an even-odd test on the target using K2 photometry. The even and odd transits are consistent with one another in transit depth to within 1σ. We also looked for secondary eclipses in the phase-folded light curve and found none to be present. Note that since we observe consistent signals in both the K2 and SPECULOOS data sets, we can rule out the signal originating from systematics. The transit depth in SPECULOOS observations with I+z filter, which is redder than Kepler bandpass, are consistent to K2 transit depths within 1σ level, keeping up with the expectation of the achromatic nature of planetary transit. Furthermore, a massive companion, such as a faint white dwarf, can be ruled out using the ellipsoidal variation, which puts a 3σ upper limit on the mass of any companions at the given orbital period of the transit signal as ∼100 MJup (Morris 1985; Niraula et al. 2018). From the transit fit, we can rule out a grazing eclipse originating from a larger transiting object (i.e. ≥ 2R⊕) at >3σ confidence. Together, these tests rule out the object at 3.14 days being a massive companion. 3.3. Transit Fitting We used the refined estimates of the host properties together with a joint analysis of the K2 and SPECULOOS light curves to derive the planetary properties. In order to calculate the transit model, we used batman (Kreidberg 2015). We simultaneously model both the K2 observation as well as the ground-based observations with 21 parameters in a Monte Carlo Markov Chain (MCMC) framework using the emcee package (ForemanMackey et al. 2013). We use a Gaussian prior on the scaled semi-major axis of the orbit a/R∗ of N (25.72, 0.27), derived using Eq. 30 from Winn (2010) along with the stellar density of 32.6 ± 1.0 g cm−3 (see Section 3.1.1) and orbital period of 3.1443 days from the TLS search. As for the limb darkening, we use the non-informative q1, q2 parameterization of the quadratic limb-darkening law as suggested by Kipping (2013). We fixed the eccentricity to 0, given that the expected time of circularization is 50 Myr (assuming a quality factor Qp ∼ 500, Goldreich & Soter 1966; Patra et al. 2017), which is at least an order of magnitude smaller than the estimated age of the system. For K2 data, we supersample the transits by a factor of 15 in batman to take into account the effect of non-negligible integration time. As for the ground-based data, we use second-order polynomials to detrend against the observables airmass and FWHM. We ran the MCMC for 50,000 steps with 150 walkers performing a combined fit of K2 and SPECULOOS — (1) This work. (2) Huber et al. (2016). (3) Stassun et al. (2019). (4) Cutri et al. (2003). (5) Gaia Collaboration et al. (2018). (6) Cutri et al. (2013). (7) This work, evolutionary model analysis. (8) This work, Keck/HIRES analysis. (9) This work, SED analysis. References data, and use the last half of the run to build the parameter posteriors. We assessed the convergence of walkers using the suggested autocorrelation test for emcee. The resulting median values from the fit with 1σ deviation are reported in Table 2, while the best-fit transit models are shown in Figure 2 and Figure 3. [2] http://kinematics.bdnyc.org/query [3] http://stdatu.stsci.edu/cgi-bin/dss form

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Analysis and Validation

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 2. OBSERVATIONS 2.1. A Candidate in Archival K2 Data EPIC 249631677 was observed by K2 in Campaign 15 from 2017-08-23 22:18:11 UTC to 2017-11-19 22:58:27 UTC continuously for about 90 days as part of program GO 15005 (PI: I. Crossfield). The pointing was maintained by using two functioning reaction wheels, while the telescope drifted slowly in the third axis due to radiation pressure from the Sun, which was corrected periodically by thruster firing (Howell et al. 2014). As a consequence of such a modus operandi, uncorrected K2 light curves can show saw-tooth structures. Many pipelines have been built to correct for such systematics. Two popular detrending algorithms for K2 light curves are K2SFF (Vanderburg & Johnson 2014) and everest (Luger et al. 2016). These pipelines have helped to achieve precision comparable to that of Kepler by correcting for systematics caused by intrapixel and inter-pixel variations. In the case of EPIC 249631677b, the standard deviation of the flattened light curve for K2SFF was observed to be 1230 ppm, compared to 685 ppm for everest. Considering this, we use the light curve from the everest pipeline, available from the Mikulski Archive for Space Telescopes, throughout this analysis. We use a biweight filter with a window of 0.75 days, as implemented in w¯otan (Hippke et al. 2019), to generate the flattened light curve for further analysis, and use only data with quality factor of 0. This light curve can be seen in Figure 1. The simple aperture photometric light curve has a scatter of 2527 ppm, which improves to 685 ppm after everest processing. We searched the flattened data for periodic transit signals using the transit least squares algorithm (TLS) (Hippke & Heller 2019), and found a prominent peak around 3.14 days as can be seen in Figure 2. We assessed the presence of additional candidate signals after modeling out the 3.14-d signal by re-running TLS, but did not find any with a significant signal detection efficiency (i.e., SDE>10). 2.2. Candidate Vetting with SPECULOOS Telescopes We followed up on the planetary candidate by observing with SPECULOOS Southern Observatory (SSO) two transit windows on UT 25 February 2020 by Ganymede and on UT 18 March 2020 by Io, and one transit window with SPECULOOS Northern Observa-. tory on UT 18 May 2020 by Artemis. SSO is composed of four telescopes: Io, Europa, Ganymede and Callisto, which are installed at ESO Paranal Observatory (Chile) and have been operational since January 2018. SNO is currently composed of one telescope (Artemis), which is located at the Teide Observatory (Canary Islands, Spain) and operational since June 2019. All SPECULOOS telescopes are identical robotic Ritchey-Chretien (F/8) telescopes with an aperture of 1-m. They are equipped with Andor iKon-L cameras with e2v 2K × 2K deep-depletion CCDs, which provide a Field of View (FoV) of 12 0 × 12 0 and the corresponding pixel scale is 0.35 00 pixel−1 (Delrez et al. 2018; Jehin et al. 2018). To schedule those windows we used the SPeculoos Observatory sChedule maKer (SPOCK), described in Sebastian et al. (in prep.). Observations were made with an exposure time of 40 s in an I+z filter, a custom filter (transmittance >90% from 750 nm to beyond 1000 nm) designed for the observation of faint red targets usually observed by the SPECULOOS survey (Delrez et al. 2018; Murray et al. 2020). SSO data were then processed using the SSO Pipeline, which accounts for the water vapor effects known to be significant for differential photometry of redder hosts with bluer comparison stars (Murray et al. 2020). SNO data were processed using prose, a Python-based data reduction package, which generates light curves from raw images (Garcia et al. in prep.). It creates a stacked image to extract the positions of the stars in the field, and uses the positions to perform aperture annulus photometry. A differential light curve is produced using a weighted light curve derived from the field stars, while the instrumental systematics, such as pointing shift and full width half maximum, are recorded to assist later in detrending. We show detrended light curves from SPECULOOS in Figure 3, where we recovered the transit events within 1σ of the calculated ephemeris from K2 data. Since these observations were obtained two years after K2 Campaign 15, they improve the precision of the transit ephemeris by an order of magnitude.

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Observation

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References ABSTRACT We report on the discovery of a transiting Earth-sized (0.95R⊕) planet around an M3.5 dwarf star at 57 pc, K2-315b. The planet has a period of ∼3.14 days, i.e. ∼π, with an instellation of 7.45 S⊕. The detection was made using publicly available data from K2’s Campaign 15. We observed three additional transits with SPECULOOS Southern and Northern Observatories, and a stellar spectrum from Keck/HIRES, which allowed us to validate the planetary nature of the signal. The confirmed planet is well suited for comparative terrestrial exoplanetology. While exoplanets transiting ultracool dwarfs present the best opportunity for atmospheric studies of terrestrial exoplanets with the James Webb Space Telescope, those orbiting mid-M dwarfs within 100 pc such as K2-315b will become increasingly accessible with the next generation of observatories. stars: individual (2MASS J15120519-2006307, EPIC 249631677, TIC 70298662, K2-315b) – planets and satellites: detection Keywords: 1. INTRODUCTION The redesigned Kepler mission, K2 (Howell et al. 2014), has been a success by adding almost 400 confirmed planets to the 2,348 discovered by the original mission[1]. Building upon Kepler (Borucki et al. 2010), K2 expanded the search of planets around brighter stars, covered a wider region of sky along the ecliptic, and studied a variety of astronomical objects . Together, these endeavors have revolutionized the field of exoplanetary science by quadrupling the number of exoplanets known at the time, while K2 in particular has led to exciting discoveries, such as disintegrating planetesimals around the white dwarf WD-1145 (Vanderburg et al. 2015), multi-planet systems around bright stars like GJ 9827 (K2-135) (Niraula et al. 2017; Rodriguez et al. 2018), and resonant chains of planets like the K2-138 system with five planets (Christiansen et al. 2018). Space-based platforms such as Kepler can provide high-quality continuous monitoring of targets above the Earth’s atmosphere. The simultaneous photometric monitoring of tens of thousands of stars enables finding rare configurations (e.g., WD-1145) and answering science questions regarding planetary populations that are more statistical in nature such as how unique our own Solar System is, or what are the most common type of planets (e.g. Fressin et al. 2013; Fulton et al. 2017). Ground-based facilities, on the other hand, often detect fewer planets while operating at a lower cost. These planets frequently exhibit larger signal-to-noise ratios (SNRs) in various metrics (e.g., transmission), thereby allowing for these planets to be characterized further. One such example is the TRAPPIST-1 planetary system (Gillon et al. 2016, 2017), discovered by the TRAPPISTUltra Cool Dwarf Transiting Survey, a prototype survey for the SPECULOOS Survey (Gillon et al. 2013). The goal of the SPECULOOS Survey is to explore the nearest ultracool dwarfs (Teff <3000 K) for transits of rocky planets (Burdanov et al. 2018; Delrez et al. 2018; Jehin et al. 2018; Sebastian et al. in prep.). Although few systems are expected (Delrez et al. 2018; Sebastian et al. in prep.), their impact on the field will be significant as they should provide most of the temperate Earthsized exoplanets amenable for atmospheric studies with the next generation of observatories such as JWST (e.g. Gillon et al. 2020). Beyond the SPECULOOS Survey, which monitors nearby late-M dwarfs for terrestrial planets, the SPECULOOS telescopes have been used to study the planetary population around mid- and late-M dwarfs. In that context, SPECULOOS facilities have been involved in following up and validating planetary candidates, notably from TESS (G¨unther et al. 2019; Kostov et al. 2019; Quinn et al. 2019). Next to confirming planetary candidates that cross detection thresholds, we have started to investigate weaker signals. For this work, we revisited K2 data, a mission which ended only in 2019. We reanalyzed the light curves of stars with Teff < 3500 K, a Kepler magnitude < 15, and a log g > 4.5. While these criteria were motivated particularly to look for planets around ultra-cool dwarfs, they were relaxed in order to allow room for errors in the stellar properties and improve completeness of the analysis. Among the 1,213 stars fitting these criteria, EPIC 249631677 presented the strongest periodic transit-like signal. In this paper, we report the discovery of an Earthsized K2 planet in a close-in orbit around EPIC 249631677. The paper is structured as follow; observations (Section 2), analysis and validation (Section 3), and the discussion in regards to future prospects for characterization (Section 4). In this paper, we report the discovery of an Earthsized K2 planet in a close-in orbit around EPIC 249631677. The paper is structured as follow; observations (Section 2), analysis and validation (Section 3), and the discussion in regards to future prospects for characterization (Section 4). [1] https://exoplanetarchive.ipac.caltech.edu

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Abstract & Intro

Forty-four percent of startups in 2022 because they ran out of cash. Extremely high burn rates, insufficient funding, poor financial planning, and overhead expenses — these things can cripple even the most brilliant companies. shut down Before joining DeckRobot, I had spent more than 5 years working in fast-growing startups, overseeing their finances and operations. This experience has led me to a conclusion: All startups tend to undermine themselves in a similar way. So I've developed basic rules to help reduce recurring expenses and fine-tune their operations. Know and track your key metrics Analyzing financial data from the very beginning provides a clear picture of a startup's financial health; it helps identify areas of high and unnecessary costs. The three key metrics to analyze from the beginning to pinpoint areas of improvement are CAC, CLV, and Gross Margin. It is the cost a business incurs to acquire a new customer. The formula for calculating it: CAC = Total Sales and Marketing Costs / Number of New Customers Acquired. Customer Acquisition Cost (CAC). If you spend $200,000 per month on advertisements that bring in 1,000 clients, your CAC amounts to $20. The definition of a "good" CAC can vary depending on industry standards, business objectives, and the competitive environment. For reference, the benchmark CAC for e-commerce is , for B2B SaaS it is $239, and for real estate, it stands at $791. $86 To determine whether this is an acceptable or unfavorable CAC, you'd need to consider other factors, including the expected CLV for a typical customer. This metric represents an estimated amount of revenue a business can generate from a single customer. Customer Lifetime Value (CLV or LTV). Its formula: CLV = (Average Purchase Value x Average Purchase Frequency) x Average Customer Lifespan. It’s considered healthy when CLV exceeds CAC. If it doesn’t, consider re-evaluating your marketing strategy. It's the percentage of revenue remaining after subtracting the cost of goods or services sold. Its formula: Gross Margin = (Total Revenue - Cost of Sales) / Total Revenue. Gross Margin. Imagine you are running a SaaS company and you want to calculate your Gross Margin. Let’s say, your total subscription revenue is $300,000. Your expenses: Hosting and maintenance costs ($60,000), customer support costs ($30,000), other expenses ($10,000). In this example, Gross Margin for the SaaS business is 66.67%. This means that, after accounting for the direct costs and other relevant operating expenses, you retain approximately 66.67% of your total SaaS subscription revenue as a gross profit at this stage. A healthy gross margin is 50-70%. Boost efficiency at your workplace Start by outlining your critical business processes and identifying bottlenecks — points where the workflow is slowed down. For instance, a developer might be overloaded with tasks, leading to missed deadlines and a decline in work quality. In such instances, introducing a part-time developer could prove beneficial. Another bottleneck example: Your employees are manually doing work that could be automated. Collecting sales and marketing data, creating email campaigns, and customer support –– these are time-consuming tasks that often can be automated. A recent suggests that automation can reduce business costs by 50-60%. study How to automate startup processes: Organize and track issues. Use JIRA, Asana, Trello, or similar services to monitor your team's performance and prevent chaos. Set up continuous integration (CI). Use Jenkins, AWS CodePipeline, CircleCI, or other CI systems that can detect issues early in the code development process, preventing costly bug fixes later on. Keep track of your finances. Monitor your income and expenses and gain insights with QuickBooks, FreshBooks, Wave, Xero, Zoho Books, or similar tools. Optimize marketing and sales. Consider using Salesforce, HubSpot, GrowBots, and Zapier to improve your marketing and sales efforts. Eliminate unnecessary expenses Every penny counts for a startup. You should regularly review your expenses and cut excessive spending. Here's what you can do to reduce costs. Labor costs consume up to of a startup's expenses. At the initial stage, most of the tasks can be managed by three employees: CEO, CTO, and CMO. Rethink your hiring strategy. 70% Early-stage startups spend of the planned annual revenue on marketing. That’s a lot, so be careful to allocate these resources wisely. Monitor the performance of your ad campaigns and confirm they yield results. Review your marketing expenses. 10% You can save a significant portion of your budget if you don’t rent a space and pay utility bills. If you need to meet in person, consider booking a meeting room at a coworking space for a few days a week, as it's a more cost-effective option. Or simply decide which team should work in person and arrange a space for the specific group. Give up your office or make it hybrid. . Maybe you don’t need a Zoom subscription if you are already paying for Slack (or just use Google Meet, which is free). See if there’s a way to use free versions of services or a single tool for various purposes. You don’t need that many subscriptions . In 95% of instances , your negotiations won't be significantly more successful just because you spend thousands of dollars on a flight, accommodations, and a fancy restaurant dinner. Choose calls over business trips (made it up, but you get the point) Start using cloud computing, analytics tools Instead of investing in expensive hardware to store your data, use cloud computing. Since these services operate on a pay-as-you-go or subscription model, you won't be charged for resources you don’t use. This could save you around of your startup’s budget. 20% Investing in data analytics tools is also beneficial for your business's finances. Poor-quality data causes of marketers to waste budget funds, while a lack of supply chain visibility poses the biggest challenge for of chief supply chain officers. Use analytics to interpret your metrics effectively, make data-driven decisions, and reduce costs. 37% 84% Initially, a simple Excel table can suffice for calculating and visualizing data. But when your startup grows and there’s more data, move to more sophisticated tools. It can be Microsoft Power BI, SAS, Apache Spark, Tableau, or Knime. A $9.99/month subscription to these services is never a waste of money.

Simple Steps to Improve Your Startup's Unit Economics and Reduce Costs

আপনার স্টার্টআপের ইউনিট অর্থনীতির উন্নতি এবং খরচ কমানোর সহজ পদক্ষেপ

Einfache Schritte zur Verbesserung der Stückökonomie Ihres Startups und zur Kostensenkung

Pasos sencillos para mejorar la economía unitaria de su startup y reducir los costos

Étapes simples pour améliorer l'économie de l'unité de votre startup et réduire les coûts

आपके स्टार्टअप की इकाई अर्थव्यवस्था में सुधार और लागत कम करने के लिए सरल कदम

スタートアップのユニットエコノミクスを改善し、コストを削減するための簡単な手順

스타트업의 단위 경제성을 개선하고 비용을 절감하는 간단한 단계

Passos simples para melhorar a economia da unidade da sua startup e reduzir custos

Простые шаги для улучшения юнит-экономики вашего стартапа и сокращения затрат

Girişiminizin Birim Ekonomisini İyileştirmek ve Maliyetleri Azaltmak için Basit Adımlar

Các bước đơn giản để cải thiện tính kinh tế của đơn vị khởi nghiệp của bạn và giảm chi phí

提高初创公司单位经济效益并降低成本的简单步骤

Let's learn about via these 21 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Alexa /Learn Repo 1. How to Hack Your Alexa using a Voice Command-SQL Injection Based on your previous coverage of similar topics, we thought you may be interested in this recently discovered voice activation device hack, and a new technique we call Voice-Command SQL Injection. 2. R.I.P. Alexa Dot Com: You Will Be Sorely Missed Amazon will be "retiring" Alexa.com on May 1, 2022. Why? 3. Does Speech Recognition Have Future? Voice recognition technology is another critical element of the voice search field. After several breakthrough innovations, the circle of speech recognition has been closing since the end of the 18th century. 4. How to Build An Alexa Home Automation System How to make an IoT-based Smart Home with Arduino IoT Cloud & Alexa using NodeMCU ESP8266 to control 4 home appliances with voice commands. 5. AI for Noobs: How Amazon Alexa Works How Amazon Alexa AI processes and implements commands. 6. The Winter for Voice Assistants in Upon Us Alexa, Google Home, and others have hit the skids. The early promise of voice assistants hasn't paid off. Time to pivot. 7. Alexa, Ask My Website How Many Bad Reviews I Have Today? Requiring access to particular information in a repetitive manner is the perfect use case for voice enabled user interface. 8. I am Grootu: Creating my First Alexa Skill As a child of the 80's I come from the age of the internet where it was similar to the wild west, only we didn't know it at the time... Men were men, women were women and girls on BBS boards were almost certainly guys in real life. 9. Voice Assistants are the Modern Day Groom of the Stool Image of : The 1st Earl of Holland, Groom of the Stool to Charles I, until 1643, 10. Amazon and Disney Take Voice Assistance to Magical Heights with “Hey Disney” Amazon & Disney teamed up to launch a new voice assistant that will be live soon in the United States. 11. 5 Ways Voice Technology Can Transform Small Business in 2021 Voice technology changed the way we surf the net and interact with things. Data suggests people are trending towards voice technology & voice-enabled activities 12. Are Smart Devices Worth the Privacy Risk? Smart devices are great. They help you search the internet with only your voice, allow you to order essentials without using your phone or computer, and let you see who's at the door. But they also do one other thing. They are listening, all the time. While Alexa is best known for this morally dubious listening, it's not just them. Google has just been shown to do the same. It seems like every smart device is listening in to your conversations. Let's talk about potential concerns and why these companies are doing this. 13. Using WordPress and Formidable Forms to Create your own Alexa Skill Form builders are among the most popular WordPress plugins. There are dozens of them and with few having millions of active installations. 14. Covert Google Voice Into Your Own Private Bouncer Or Receptionist In the event that you don’t have a Google Voice telephone number yet, you’re passing up a great opportunity. Google Voice has some extraordinary highlights that can help ensure your security. Also, you can keep your Google Voice telephone number forever, or for in any event insofar as Google is eager to have it. 15. How To Launch Your Podcast On Alexa Devices (Amazon, Google Nest & Apple’s HomePod) The beauty of podcasts is that they’re incredibly portable and easy to consume. People can listen to your Podcast on Alexa devices, so here is how to start! 16. Smart Teaching: Using Alexa in The K-12 Classroom In many K-12 classrooms, the ability to access information by saying it out loud is changing the way students experience and interpret things. 17. How to Build a Voice App in 30 Minutes You have mastered building web apps. Maybe even building mobile apps. But what about these smart speakers that are now sitting in people's living rooms? What about our new found ability to talk to apps on our phones? 18. What You Need To Build A Custom Alexa Skill For Your Website Do you know how many people are asking Alexa about your website/business? 19. Amazon Engineer Jackson Kelley is Working on Data Privacy in Alexa Ex block.one consultant (and formerly ML infrastructure engineer in retail at Amazon) Jackson Kelley has been nominated for a 2020 Noonie for contributions to the sticky subject of politics here on Hacker Noon. Scroll on for a a summary of what Jackson's been doing and thinking lately, including a new YouTube series, and which of his controversial opinions most people would definitely disagree with. 20. 3 Ways to Find a Lost Phone Using Amazon Alexa Everyone dreads losing their telephone. With Alexa and Amazon Echo, you may never need to stress over this again. There are a few diverse applications and IFTTT arrangements you can use to help find your mobile phone when you've misplaced it. Here are a couple of our top choices and how to get them set up. 21. 5 Tech Accessories That Make Alexa Smarter Sonos One (Gen 2) Thank you for checking out the 21 most read stories about Alexa on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

21 Stories To Learn About Alexa

324 Stories To Learn About Development

This Job Training Program Is Helping People With Autism Fight Cybercrime

Industrial robots have been helping manufacture products and facilitate logistics for more than 60 years. Traditionally, these robots are very good at a specific function — for instance, going from A to B. Throw an obstacle into their work environment however — be it a physical object or a human collaborator — and severe safety hazards arise. Technology is progressing such that today’s industrial robots are being designed to be “adaptive,” meaning that they’re ​​capable of adapting to and learning from their environment, including human interactions. The big vision is that humans and automated robots will be able to interact with one another on a machine shop floor, reducing the risk involved with having humans work side by side with giant machines. In Europe, industrial robots are becoming more commonplace. Data published in 2021 from The International Federation of Robotics showed European industrial robot installations up 24% compared to 2020. And while Europe still trails the United States and China in terms of industrial robot use, the growth of EU industrial production — up 8% in 2021 and another 5% in 2022 — could drive robot adoption in factories. In order to better ensure that the industrial robots of the future can readily adapt to changing environments, while maintaining safe and trustworthy collaboration with humans, a group of universities, tech accelerators and private research labs from the European Union (EU) have established RoboSAPIENS. The project, which is funded by the European Union’s Horizon Europe 2021-2027 research and innovation programme, will develop and implement safety mechanisms that will empower robots to — at a high level — safely adapt to unexpected changes. In an interview with TheNextWeb’s Ioanna Lykiardopoulou, consortium partner Ana Cavalcanti, a professor at the University of York, said that the project aims to include a trustworthiness checker in the current Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) framework for self adaptive systems. “What is missing here is the inclusion in the loop of a step that verifies whether certified guarantees, of safety or trustworthiness, are kept when plans change,” Cavalcanti told TheNextWeb. By adding this step, researchers hope that if a robot encounters an obstacle, it will consult if its originally programmed plan is still valid with the introduction of the obstacle. If it is not, the system will self update and a “trustworthiness checker” will validate the changes for safety issues. The researchers will use a combination of Deep Learning (DL) and Digital Twin (DT) — or a virtual representation of a physical object — techniques to generate the new control settings. “As the EU advances its industrial robotics capabilities, it’s imperative that measures be taken to ensure the safety and trustworthiness of the next generation of robots working alongside humans in a collaborative environment,” said Peter Gorm Larsen, a Professor in the Department of Electrical and Computer Engineering at Aarhus University, and the coordinator of the RoboSAPIENS project. “With RoboSAPIENS, we will prepare Europe’s industrial robotics industry for rapid and unforeseen adaptability in system structure and environment, assuring trustworthy collaboration with humans by changing behavior while maintaining and even enhancing performance and safety,” he added. According to a statement from the consortium, the project is already working on industrial use studies. The first empowers robots to disassemble electronic consumer waste, another aims to improve navigation predictability of autonomous water vessels. A third looks to help Autonomous Mobile Robots (AMRs) adapt to anomalies on machine shop floors, and a fourth will automatically assess risks in human-robot collaboration (cobot) situations. Consortium partners include Denmark’s Aarhus University; Aristotle University of Thessaloniki, AUTH (Greece); Danish Technological Institute, DTI (Denmark); Fraunhofer IFF, Fraunhofer (Germany); University of York, UoY (United Kingdom); University of Antwerp, UA (Belgium); Norwegian University of Science and Technology, NTNU (Norway); PAL Robotics, PAL (Spain); ISDI Accelerator, ISDI (Spain); Simula Research Lab, SRL (Norway). The RoboSAPIENS consortium isn’t the only entity working on marketing self adaptive robots safer and more efficient. Earlier this year, Google’s Deep Mind announced a series of releases related to industrial robots, including Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) which helps robots learn and perform new tasks at more efficient speeds. And last year, a team of Japanese researchers developed a learning framework to train robots to “sense” their surrounding environment, making it safer for human-robot collaboration. Industrial robots have been helping manufacture products and facilitate logistics for more than 60 years. Traditionally, these robots are very good at a specific function — for instance, going from A to B. Throw an obstacle into their work environment however — be it a physical object or a human collaborator — and severe safety hazards arise. Technology is progressing such that today’s industrial robots are being designed to be “adaptive,” meaning that they’re ​​capable of adapting to and learning from their environment, including human interactions. The big vision is that humans and automated robots will be able to interact with one another on a machine shop floor, reducing the risk involved with having humans work side by side with giant machines. In Europe, industrial robots are becoming more commonplace. Data published in 2021 from The International Federation of Robotics showed European industrial robot installations up 24% compared to 2020. And while Europe still trails the United States and China in terms of industrial robot use, the growth of EU industrial production — up 8% in 2021 and another 5% in 2022 — could drive robot adoption in factories. published growth of EU industrial production In order to better ensure that the industrial robots of the future can readily adapt to changing environments, while maintaining safe and trustworthy collaboration with humans, a group of universities, tech accelerators and private research labs from the European Union (EU) have established RoboSAPIENS . RoboSAPIENS The project, which is funded by the European Union’s Horizon Europe 2021-2027 research and innovation programme, will develop and implement safety mechanisms that will empower robots to — at a high level — safely adapt to unexpected changes. In an interview with TheNextWeb ’s Ioanna Lykiardopoulou, consortium partner Ana Cavalcanti, a professor at the University of York, said that the project aims to include a trustworthiness checker in the current Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K) framework for self adaptive systems. interview TheNextWeb “What is missing here is the inclusion in the loop of a step that verifies whether certified guarantees, of safety or trustworthiness, are kept when plans change,” Cavalcanti told TheNextWeb . TheNextWeb By adding this step, researchers hope that if a robot encounters an obstacle, it will consult if its originally programmed plan is still valid with the introduction of the obstacle. If it is not, the system will self update and a “trustworthiness checker” will validate the changes for safety issues. The researchers will use a combination of Deep Learning (DL) and Digital Twin (DT) — or a virtual representation of a physical object — techniques to generate the new control settings. “As the EU advances its industrial robotics capabilities, it’s imperative that measures be taken to ensure the safety and trustworthiness of the next generation of robots working alongside humans in a collaborative environment,” said Peter Gorm Larsen, a Professor in the Department of Electrical and Computer Engineering at Aarhus University, and the coordinator of the RoboSAPIENS project. “With RoboSAPIENS, we will prepare Europe’s industrial robotics industry for rapid and unforeseen adaptability in system structure and environment, assuring trustworthy collaboration with humans by changing behavior while maintaining and even enhancing performance and safety,” he added. According to a statement from the consortium, the project is already working on industrial use studies. The first empowers robots to disassemble electronic consumer waste, another aims to improve navigation predictability of autonomous water vessels. A third looks to help Autonomous Mobile Robots (AMRs) adapt to anomalies on machine shop floors, and a fourth will automatically assess risks in human-robot collaboration (cobot) situations. Consortium partners include Denmark’s Aarhus University; Aristotle University of Thessaloniki, AUTH (Greece); Danish Technological Institute, DTI (Denmark); Fraunhofer IFF, Fraunhofer (Germany); University of York, UoY (United Kingdom); University of Antwerp, UA (Belgium); Norwegian University of Science and Technology, NTNU (Norway); PAL Robotics, PAL (Spain); ISDI Accelerator, ISDI (Spain); Simula Research Lab, SRL (Norway). The RoboSAPIENS consortium isn’t the only entity working on marketing self adaptive robots safer and more efficient. Earlier this year, Google’s Deep Mind announced a series of releases related to industrial robots, including Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) which helps robots learn and perform new tasks at more efficient speeds. And last year, a team of Japanese researchers developed a learning framework to train robots to “sense” their surrounding environment, making it safer for human-robot collaboration. announced developed

European Researchers Enhance Human-Robot Collaboration with Self-Adaptive Industrial Robots

Celebrating 10 Years of War Robots and Reflecting From A Technical Perspective

যুদ্ধের রোবটদের 10 বছর উদযাপন করা এবং একটি প্রযুক্তিগত দৃষ্টিকোণ থেকে প্রতিফলিত করা

10 Jahre Kriegsroboter und ein Blick aus technischer Perspektive

Celebrando 10 años de robots de guerra y reflexionando desde una perspectiva técnica

Célébration de 10 ans de robots de guerre et réflexion d'un point de vue technique

युद्ध रोबोट के 10 साल का जश्न और तकनीकी परिप्रेक्ष्य से चिंतन

戦争ロボット10周年を祝い、技術的な観点から振り返る

전쟁 로봇 10주년을 기념하고 기술적 관점에서 성찰

Comemorando 10 anos de robôs de guerra e refletindo de uma perspectiva técnica

Празднование 10-летия боевых роботов и размышления с технической точки зрения

Savaş Robotlarının 10. Yılını Kutlamak ve Teknik Açıdan Düşünmek

Kỷ niệm 10 năm robot chiến tranh và suy ngẫm từ góc độ kỹ thuật

庆祝战争机器人十周年并从技术角度进行反思

‘ai tools’ Image created by HackerNoon AI Image Generator Hey Hacker, Unless you’ve been living under a rock, you know that AI tools have been making some big waves, flooding the digital works with AI-generated content. Whether you’re an AI supporter or hater, you can’t deny it’s been helping us see the world from a different angle! Let’s cut to the chase: as OpenAI announces the arrival of GPT-4 and boasts about its ability to problem solve with greater accuracy, the HackerNoon community gets busy chatting about all things AI, such as programming tools, text processors, and AI applicationsin general. We're such nerds that we've even added a fancy new AI content detector to our editor🧐. As always, we’re excited to hear YOUR opinions on these topics. We know, writer’s block can catch even the best of us. So we put together some idea prompts to get you started on your next opinion piece: Will AI tools change the way educators evaluate student work? Do you think AI tools are ready to be used as educational resources? Share your thoughts here.


How do you think the big streamer services like YouTube and Netflix will be impacted by the new generative video tools? Write about it here.


AI-generated visual art often has a distinguishable appearance - will the art world designate a new classification to this type of art form? Tell us about it using this template.


Is Chat-GPT really gonna help you become a better programmer? Is AI even good at web development, or are people just delusional? Spill the tea here!


What new AI tools are you inspired by? Tell us about it here.
We can see the wheels turning already - click one of the links above and get writing!
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We KNOW you’re going to serve us some juicy gossip on these new technologies - can’t wait to spread it all over HackerNoon’s homepage!Happy writing,The HackerNoon Editorial Team ‘ai tools’ Image created by HackerNoon AI Image Generator ‘ai tools’ HackerNoon AI Image Generator Hey Hacker, Unless you’ve been living under a rock, you know that AI tools have been making some big waves, flooding the digital works with AI-generated content. Whether you’re an AI supporter or hater, you can’t deny it’s been helping us see the world from a different angle! Let’s cut to the chase: as OpenAI announces the arrival of GPT-4 and boasts about its ability to problem solve with greater accuracy, the HackerNoon community gets busy chatting about all things AI , such as programming tools , text processors , and AI applications in general. GPT-4 all things AI programming tools text processors AI applications We're such nerds that we've even added a fancy new AI content detector to our editor🧐. new AI content detector As always, we’re excited to hear YOUR opinions on these topics. We know, writer’s block can catch even the best of us. So we put together some idea prompts to get you started on your next opinion piece: As always, we’re excited to hear YOUR opinions on these topics. We know, writer’s block can catch even the best of us. So we put together some idea prompts to get you started on your next opinion piece: YOUR opinions Will AI tools change the way educators evaluate student work? Do you think AI tools are ready to be used as educational resources? Share your thoughts here. How do you think the big streamer services like YouTube and Netflix will be impacted by the new generative video tools? Write about it here. AI-generated visual art often has a distinguishable appearance - will the art world designate a new classification to this type of art form? Tell us about it using this template. Is Chat-GPT really gonna help you become a better programmer? Is AI even good at web development, or are people just delusional? Spill the tea here! What new AI tools are you inspired by? Tell us about it here.
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We KNOW you’re going to serve us some juicy gossip on these new technologies - can’t wait to spread it all over HackerNoon’s homepage!Happy writing,The HackerNoon Editorial Team Will AI tools change the way educators evaluate student work? Do you think AI tools are ready to be used as educational resources? Share your thoughts here. Will AI tools change the way educators evaluate student work? Do you think AI tools are ready to be used as educational resources? Share your thoughts here . here How do you think the big streamer services like YouTube and Netflix will be impacted by the new generative video tools? Write about it here. How do you think the big streamer services like YouTube and Netflix will be impacted by the new generative video tools? Write about it here . here AI-generated visual art often has a distinguishable appearance - will the art world designate a new classification to this type of art form? Tell us about it using this template. AI-generated visual art often has a distinguishable appearance - will the art world designate a new classification to this type of art form? Tell us about it using this template . template Is Chat-GPT really gonna help you become a better programmer? Is AI even good at web development, or are people just delusional? Spill the tea here! Is Chat-GPT really gonna help you become a better programmer? Is AI even good at web development, or are people just delusional? Spill the tea here ! here What new AI tools are you inspired by? Tell us about it here. We can see the wheels turning already - click one of the links above and get writing! Some Added Writing Tips to Make Your Stories Stand Out Like Skinny Jeans on a Millenial: Check out the active writing contests and tag your story to win prizes 🥇
You can use AI tools, but disclose which sections were written this way 🦾 We KNOW you’re going to serve us some juicy gossip on these new technologies - can’t wait to spread it all over HackerNoon’s homepage! Happy writing, The HackerNoon Editorial Team What new AI tools are you inspired by? Tell us about it here . here We can see the wheels turning already - click one of the links above and get writing! Some Added Writing Tips to Make Your Stories Stand Out Like Skinny Jeans on a Millenial: Check out the active writing contests and tag your story to win prizes 🥇 You can use AI tools, but disclose which sections were written this way 🦾 Check out the active writing contests and tag your story to win prizes 🥇 writing contests You can use AI tools, but disclose which sections were written this way 🦾 We KNOW you’re going to serve us some juicy gossip on these new technologies - can’t wait to spread it all over HackerNoon’s homepage! Happy writing, The HackerNoon Editorial Team

Don't Let the Robots Take Over: Share Your AI Stories Today

For those attending NVIDIA’s annual developer conference on Monday, the company’s new announcement must have been exciting.  Nvidia unveiled Project GR00T, an ambitious plan to release a software and hardware platform that can be used to build human-like robots which will continue to learn through generative artificial intelligence (AI). The company says its new launch will enable robots to be smarter and more functional than ever before – and they'll accomplish this by watching humans. The platform is made up of a computer system to power the robot and AI, along with a suite of software that includes various tools like genAI, to build human-like robots. According to NVIDIA, Project GR00T is a “general-purpose foundation model for humanoid robots, designed to further its work driving breakthroughs in robotics and embodied AI.” “Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” said Jensen Huang, founder and CEO of NVIDIA. “The enabling technologies are coming together for leading roboticists around the world to take giant leaps towards artificial general robotics.” NVIDIA says it is building a comprehensive AI platform for leading humanoid robot companies such as 1X Technologies, Agility Robotics, Apptronik, Boston Dynamics, Figure AI, Fourier Intelligence, Sanctuary AI, Unitree Robotics and XPENG Robotics, among others. Jim Fan a research manager and lead of embodied AI at Nvidia that through
GR00T, robots will be able to understand instructions through language, video
and demonstrations to perform a variety of tasks. posted to X “Today is the beginning of our moonshot to solve embodied AGI in the physical world. I’m so excited to announce Project GR00T, our new initiative to create a general-purpose foundation model for humanoid robot learning. The GR00T model will enable a robot to understand multimodal instructions, such as language, video, and demonstration, and perform a variety of useful tasks. We are collaborating with many leading humanoid companies around the world, so that GR00T may transfer across embodiments and help the ecosystem thrive. GR00T is born on NVIDIA’s deep technology stack. We simulate in Isaac Lab (new app on Omniverse Isaac Sim for humanoid learning), train on OSMO (new compute orchestration system to scale up models), and deploy to Jetson Thor (new edge GPU chip designed to power GR00T). Announced in Jensen's keynote, Project GR00T is a cornerstone for the “Foundation Agent” roadmap of the newly founded GEAR Lab. At GEAR, we are building generally capable agents that learn to act skillfully in many worlds, virtual and real… Join us on the journey to land on the moon.” What makes this project so exciting is the fact that it opens an entirely new horizon and vision for the future of robotics, GenAI, and human capability. This technology, coupled with robots, is expected to assist humans in accomplishing more in the future. Whether in healthcare, home life, logistics, travel, manufacturing, space exploration, or the military, robots equipped with GenAI will likely make our lives incredibly easier and more advanced. This next generation of robotics and computing is likely to lead to incredible breakthroughs, providing humans with super-human capabilities that only robots and advanced technology can provide. NVIDIA’s announcement is exciting. We can expect great inventions and capabilities to emerge from this technology over the next decade. People beware; the robots are coming!

Project GR00T and Its Ambition to Influence Robotics

Automation systems have radically changed the face of industries, revolutionizing their potential and enabling them to attain higher levels of efficiency, productivity, and profitability. With progress in technology and the emergence of smart manufacturing, automation systems have become a crucial driver of success for businesses across a variety of sectors. The manufacturing sector is undergoing a major transformation, and the growth of robotics is at the center of this change. Industrial robots are a crucial part of contemporary manufacturing processes as technology advances because they provide efficiency, accuracy, and versatility. In this article, we examine the beneficial effects of robotics on manufacturing and examine the challenges and impacts. According to a recent McKinsey Global Institute analysis the quick evolution of Artificial intelligence and robots by 2030. According to the research notes those losses could be offset by a boost in productivity, economic growth, and other factors. However, sustaining full employment will probably be extremely difficult because it would necessitate significant changes to the labor market and economy. could eliminate 73 million American jobs Benefits of Automation in Industries Robotic manufacturing systems may be a relatively new idea for some segments of the manufacturing industry, although the technology has been around for decades. By combining traditional production methods with higher forms of technology, factory managers and business owners can greatly raise their output rates and improve their bottom line. Robots have impacted manufacturing in a variety of positive ways. The manufacturing has a wide-ranging effect, boosting productivity and success for the entire organization. When human workers are relieved from activities that robots can complete with ease, they can devote more of their attention to contributing much-needed knowledge and ideas within higher organizational goals. When implemented correctly, robotics creates an undisputable improvement shift within an organization's operations. impact of automated Production improvement: Automating tasks allows not only greater efficiency in the process by being able to perform various tasks more quickly but also production can be maintained 24 hours a day. Higher quality: Automating tasks allows not only greater efficiency in the process by being able to perform various tasks more quickly but also production can be maintained 24 hours a day. Cost savings: Although automation can involve an initial investment, it also allows great savings. This is caused by several factors, such as fewer errors, which makes the product more cost-effective, and reduces the amount of waste that mistakes create. Information: An automated system allows the collection and storage of a large amount of information regarding the process. This makes it easy to perform analyses that can help further improve the process and make it more efficient. Safety: By automating the most difficult tasks, people are prevented from being put into dangerous situations. Furthermore, automated systems have excellent security measures that help to avoid accidents from occurring. Flexibility: By having greater control of the systems, they can more easily adapt to different situations. As an increased amount of work, or the introduction of fresh elements in the production. Less supervision: Once the system is validated, the appearance of problems in it is very rare, and in case of situations, the systems are prepared to notify them. They therefore require less supervision of their operation. Challenges and Concerns of Automation in Industries The high costs of implementation are one of the main obstacles when deploying industrial robotics. The initial integration procedure will be time-consuming, arduous, and costly. The specifications of new demands may require redesigning the workspace and repurposing robot workers. A specialized integrator may be required for even small changes to a production line. Costs: Trying to integrate new robotic systems with existing infrastructures and technologies is challenging. Smaller-scale SMEs frequently find the expenses too high or prohibitive. To recover the initial investment, production quantities and sales levels must also be sustained for the duration of the anticipated ROI period. This lack of cooperation is another challenge in industrial robotics. A single manufacturer will not only provide its hardware but also software solutions. Then, to coordinate across various devices, a variety of application programming interfaces are required, which may even call for specialized software. The possible roles of robots are limited by the inflexibility of current robotics technologies. Even skilled and experienced line workers are frequently unable to enhance manufacturing processes by this means. Inflexibility: New technologies call for new protocols, and these immediately introduce new safety hazards into the workplace. Industrial robots are no exception and strict regulations surrounding robotic safety, as well as severe penalties. Safety Concerns: Before integrating any industrial robot system, manufacturers need to be ready and have a safe workplace for workers that ensures full compliance. Investment in industrial robots would be encouraged if businesses could achieve an optimal balance between safety, versatility, and speed. Operators must also pick up new knowledge and skills. Employees usually lack the knowledge to operate new types of machinery and, in the case of mobile robots, they don’t know how to behave correctly around them. They often obstruct the way of a robot’s path or are confused about the allocation of tasks between humans and automated workers. Workforce Skills: There isn't the same embedded academic training as with older skills because the technology is still relatively new. Many workers will therefore require training, and in the meantime, it will be necessary to hire additional staff who already possess the necessary education, certifications, and experience. Industrial robots are intended to improve a facility’s entire performance. This implies that they must interact with people in a mutual dependency. Until staff obtains the essential skills, the facility will be much less efficient and therefore less cost-effective. Basic robotics training will increase worker's awareness of how robotic systems behave and how humans should react. Emphasizing the mutual reliance between industrial robotics and humans can lead to an increase in overall efficiency. Workforce Training: This can be assisted by using advanced analytics and data collection to identify key issues as much data as possible should be gathered by operators, such as the number and kind of journey undertaken by robotic systems, downtime resulting from errors and malfunctions, battery charging durations, and idle times. Utilizing this information, operators can modify the necessary procedures to target certain problems and boost robot efficiency. Product workflows need to be assessed to see what benefits could be made by utilizing robots. This involves calculating the speed and orientation of part presentation to robots to ensure production is maximized, without overloading existing systems. Anecdotal and experiential evidence from workers can be analyzed by using data to enhance workflow and assess how their contributions impact design and integration processes. This could also assist in assessing the total impact on performance and production cycles, to get rid of inefficiency. Managing Workflows: Even though they have a wide range of uses, industrial robotics problems are often similar. With all the technological advancements in Industry that enable modern robotic systems, many of these problems are being resolved. Latest Advances: Frequent training will boost people's comprehension of robotic platform operation. In the long run, more people will acquire the requisite skills and could learn how to develop their robotic platforms. Both no-code and open-source programming environments are being used in new programming solutions. The Future of Work in a Robotic Society Automated systems can identify inefficiencies that a human mind could unintentionally overlook, while AI-driven systems help ensure no errors. Consequently, there may be a decrease in the demand for supervisory positions whose primary objectives are quality assurance and productivity management. These digitally-driven systems also enable workers to have short breaks and more freedom from jobs that have become more intense and stressful in current times. This is the most common characteristic of robotics and automation adoption: the augmentation of human labor, rather than its replacement. This technique paves the way for the most ideal situation – humans and robots collaborating, empowering, and transforming the workforce as a whole. The fear that robots will take over human employements, and become the boss, stands invalid. The digital workforce facilitates efficient management of resources – different industries leverage AI-driven solutions to solve the shortage of human resources and promote improved customer service. One example we see frequently: is a new service model that integrates robotics and automation with e-commerce to provide more personalized services. Conclusion Production operations are now controlled by industrial automation. In some ways, the implementation of this system tends to be a substitute for the work done by humans. In short, replacing human beings in an industry. The time consumed on manufacturing has been deducted since robots never get distressed and are capable of engaging in risky jobs. Therefore, labor opportunities may be decreased by up to 50% as it turned automation technologies are budget-friendly. Nevertheless, technology was proven to create jobs rather than destroy the opportunity through robotics, blockchain technology, artificial intelligence, telecommunication, and more. Any size business could find enormous success by implementing automation.

Robots at Work: How Automation is Transforming Industries

This paper is under CC 4.0 license. available on arxiv : Authors (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References CONCLUSION The analysis of the interactions and dialogues that took place during the therapeutic laboratory for autonomies endorsed with the inclusion of the Pepper robot provided an interesting perspective on the communication dynamics of children in the lab. The results obtained showed the effectiveness of the robot in promoting autonomy and functional acquisition, demonstrating that the therapeutic method based on assistive robotics is a valuable resource to support the rehabilitation needs of communication and social skills of autistic children. The analysis highlights new possibilities for their engagement and active participation as a co-designer, as we already experienced in the past with neuro-typical children [19], representing an innovative opportunity to promote the development and wellness of autistic people and opening to new perspectives for therapeutic intervention more aware of the needs of autistic minds. In conclusion, this paper highlights the need to provide the robot with the ability of adapt to the children’s peculiarity and features and sharing her/his vocabulary, especially by recognizing and using memes during interactions, to inspire greater trust in children and allowing the use of common slang, already in use with classmates, allowing them to consider Pepper a peer and effectively insert it in the role of mediator. In addition, it would allow children to use a simplified type of communication during meetings to compensate for the typical communication deficits associated with autism spectrum disorders. In the future we will work in this directions (co-design, user’s adaptations, using and recognizing memes during children-robot communication) having the robot able to communicate and express adapting to user tastes and preferences and and try again to field a real-world evaluation that takes into account the effectiveness of different levels of user adaptation [23]. REFERENCE [1] M. Biondi e M. Maj, DSM-5: diagnostic and statistical manual of mental disorders: text revision, 5. ed. Milano: Raffaello Cortina. [2] E. Hollander, R. Hagerman, e C. Ferretti, Textbook of Autism Spectrum Disorders, Second Edition. American Psychiatric Association Publishing, 2022. [3] ISS - Istituto superiore di sanità, «Osservatorio Nazionale Autismo», OssNA. https://osservatorionazionaleautismo.iss.it [4] P. Pennisi et al., «Autism and social robotics: A systematic review», Autism Research, vol. 9, fasc. 2, pp. 165–183, 2016, doi: 10.1002/aur.1527. [5] S. Shamsuddin, H. Yussof, S. Mohamed, e F. A. Hanapiah, «Design and Ethical Concerns in Robotic Adjunct Therapy Protocols for Children with Autism», Procedia Computer Science, vol. 42, pp. 9–16, gen. 2014, doi: 10.1016/j.procs.2014.11.027. [6] K. Dautenhahn e I. Werry, «Towards Interactive Robots in Autism Therapy: Background, Motivation and Challenges», Pragmatics & Cognition, vol. 12, pp. 1–35, giu. 2004, doi: 10.1075/pc.12.1.03dau. [7] C. Li, Q. Jia, e Y. Feng, «Human-Robot Interactoin Design for Robot-Assisted Intervention for Children with Autism Based on E-S Theory», in 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China: IEEE, ago. 2016, pp. 320–324. doi: 10.1109/IHMSC.2016.103. [8] A. Peca et al., «Robot Enhanced Therapy for Children with Autism Disorders: Measuring Ethical Acceptability», IEEE Technol. Soc. Mag., vol. 35, fasc. 2, pp. 54–66, giu. 2016, doi: 10.1109/MTS.2016.2554701. [9] B. Robins, K. Dautenhahn, e J. Dubowski, «Does appearance matter in the interaction of children with autism with a humanoid robot?», IS, vol. 7, fasc. 3, pp. 479–512, nov. 2006, doi:10.1075/is.7.3.16rob. [10] E. S. Kim, «Robots for social skills therapy in autism: evidence and designs toward clinical utility», 2013, doi: 10.13140/2.1.3038.2083. [11] «Autismo», Neuraxpharm Italia. https://www.neuraxpharm.com/it/disordini/autismo [12] L. Hull, K. V. Petrides, e W. Mandy, «The Female Autism Phenotype and Camouflaging: a Narrative Review», Rev J Autism Dev Disord, vol. 7, fasc. 4, pp. 306–317, dic. 2020, doi: 10.1007/s40489-020-00197-9. [13] D. Cohen et al., «NAO, a humanoid robot as a therapeutic mediator for young people with autism». [14] Cristina Gena, Claudio Mattutino, Andrea Maieli, Elisabetta Miraglio, Giulia Ricciardiello, Rossana Damiano, Alessandro Mazzei: Autistic Children's Mental Model of an Humanoid Robot. UMAP (Adjunct Publication) 2021: 128-129 [15] Rossana Damiano, Cristina Gena, Andrea Maieli, Claudio Mattutino, Alessandro Mazzei, Elisabetta Miraglio, Giulia Ricciardiello: UX Personas for Defining Robot's Character and Personality 213-216. IUI Workshops 2022: 213-216. [16] Cristina Gena, Claudio Mattutino, Stefania Brighenti, Andrea Meirone, Francesco Petriglia, Loredana Mazzotta, Federica Liscio, Matteo Nazzario, Valeria Ricci, Camilla Quarato, Cesare Pecone, Giuseppe Piccinni: et al., Sugar, Salt & Pepper - Humanoid robotics for autism, in Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26th ACM Conference on Intelligent User Interfaces (ACM IUI 2021), College Station, United States, April 13-17, 2021, D. Glowacka e V. R. Krishnamurthy, A c. di, in CEUR Workshop Proceedings, vol. 2903. CEUR-WS.org, 2021. [17] Cristina Gena, Rossana Damiano, Claudio Mattutino, Alessandro Mazzei, Andrea Meirone, Loredana Mazzotta, Matteo Nazzario, Valeria Ricci, Stefania Brighenti, Federica Liscio, Francesco Petriglia: Preliminary results of a therapeutic lab for promoting autonomies in autistic children. arXiv, doi: 10.48550/arXiv.2305.02982. [18] B. C. Carbo, «The Use of Social Stories with Individuals with Autism Spectrum Disorders», University of Delaware ProQuest Dissertations Publishing, Newark, DE, 2005. [Online]. Disponibile su: https://udspace.udel.edu/server/api/core/bitstreams/cd5e8850-e57c-41d2-a7b4ac5e25a4b659/content [19] V. Cietto, C. Gena, I. Lombardi, C. Mattutino and C. Vaudano, "Co-designing with kids an educational robot," 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Genova, Italy, 2018, pp. 139-140, doi: 10.1109/ARSO.2018.8625810. [20] A. Mousa, «Sherry Turkle, Reclaiming Conversation. The Power of Talk in a Digital Age», Questions de communication, fasc. 34, Art. fasc. 34, dic. 2018, doi:10.4000/questionsdecommunication.16891. [21] H.-M. Chiang e M. Carter, «Spontaneity of Communication in Individuals with Autism», J Autism Dev Disord, vol. 38, fasc. 4, pp. 693–705, apr. 2008, doi: 10.1007/s10803-007-0436-7. [22] Gena, C., Cena, F., Vernero, F., Grillo, P. The evaluation of a social adaptive website for cultural events. User Model User-Adap Inter 23, 89–137 (2013) https://doi.org/10.1007/s11257-012-9129-9 [23] Gena, Cristina, and Ilaria Torre. "The importance of adaptivity to provide onboard services: A preliminary evaluation of an adaptive tourist information service onboard vehicles." Applied Artificial Intelligence 18.6 (2004): 549-580.

The Power of Memes: The Pepper Robot as a Communicative Aid for Autistic Children - Conclusion

This paper is under CC 4.0 license. available on arxiv Authors: (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References MEMES The qualitative analysis of the material produced during the project underlines that there is a linguistic barrier between Pepper and the children because Pepper's dialogue system was written by adults, while children talk through a vocabulary peculiar to their age. What’s more relevant is that the participants integrate the use of memes into everyday conversations, which is quite surprising, even considering that’s common practice between teens and pre-teens, if we consider the textbook definition of Autism Spectrum Disorders, since this requires the ability to master the underlying social context in the information presented just as a couple words-image matched through, usually ironic and sarcastic, rhetorical figures based on pop culture. Although communicating through memes offers a few advantages that directly compensate for the difficulty autistic people face while entering social situations, such as: ● Regulate using echolalia in stressful and anxious situations, such as encountering people in public spaces, hidden in the repetition of a sound considered more socially acceptable, such as the meme’s catchphrase. ● Transmission of disambiguous information: Memes, being a word-image pair, provide access to a concise and simplified non-verbal communication system that implies a broader meaning that cannot be misunderstood by those who know the sharing context. It provides a more accessible and comfortable way to express a concept than the risky natural communication that can cause misunderstandings. ● Sense of social belonging: the nature of a meme is that its information shared widely within online communities. This creates a sense of belonging to the social group of people who participate in certain platforms and applications. When a child enunciates a catchphrase, he assumes that his audience knows the meme from which it is taken and therefore understands what image it should be associated with, even though it is not shown (and vice versa). Receiving a response from the rest of the group constitutes an implicit expression of consent and confirmation of belonging to the same social group (those who know the meme), establishing mutual understanding and a friendly connection with others. ● Expression of emotions: memes are concentrated and concise formulas for the exposition of much broader, complex, and layered concepts, which, typically, autistic people find difficult to identify and express. This extreme simplification in a word-image pair allows children to identify their moods, more easily represent their emotional experiences, and communicate concepts that would be too complex for them to explain through traditional verbal language. In the same way, these children usually communicate with each other using gestures referencing the images or videos presented in popular memes, expecting the group to catch the related tagline to understand what they’re trying to express. The usage of memes was so common during the laboratory that therapists suggested creating a list of “forbidden words”, updated at the beginning of every encounter, containing all the words so overpronounced to be considered distracting from the children themselves. That ended up being a collection of meme catchphrases the participant admitted using so frequently to be an impairment to maintain a correct level of participation during the activities. Updating the list at the beginning of every session prompted the children to be more self-conscious about their habits and what they found distracting; it also encouraged them to improve their vocabulary and communication skills to express themselves without using those memes. On the other hand, introducing this activity before starting with the program for the day helped them settle into the laboratory, slightly extending the familiarization process begun in the welcome phase. Taking advantage of echolalia, as the children's relief associated with the repetition of words and sounds that are reassuring to them, is comforting for them and makes them aware they are entering a safe space, where they will receive help to improve their skills in a way that understands the peculiarity of their minds and free of any prejudice. According to this observation, in future applications, Pepper should be able to understand the meaning behind gestures, especially basic ones, such as hand raising, to catch non-verbal inputs, and when the children are referencing a meme and how to implement those into the dialog, to fully understand what is being communicated by the children and increase the perception of trust, credibility, and belonging to the same social group, helping the children to see Pepper as their peer and effectively use it in the role of mediator. To implement this kind of information, it can be useful to involve a group of teenagers and pre-teens, or a content creator who specializes in making content for this age group, in the process of making the robot persona and creating the dialogues. Another way to do this can be by leveraging machine learning strategies already planned to improve the quality of conversations, allowing the robot to learn directly from the children participating in the project, who demonstrated to be interested in helping with improving the robot and explaining their slang, recording the implicit meaning of a pun or a gesture for future reference and usage during the interactions. The use of machine learning also opens up the opportunity to dip into the websites most frequently used by this age group, such as social networks and video game forums. This solution can be helpful to keep Pepper’s vocabulary updated but is certainly more challenging due to the amount of data collected by this platform and the difficulty in designing a strategy to make the robot understand the subtext of the meme and to keep up with the evolution of the trends, even considering the possibility of relying on online databases that are updated frequently and that aim precisely to explain the meaning of this type of content, such as knowyourmeme.com. Finally, it is important to consider that the robot will interact with children, augmenting the risk of mistakenly implementing inappropriate content into the robot, considering that the majority of the information stored online is not child friendly.

The Power of Memes: The Pepper Robot as a Communicative Aid for Autistic Children - Memes

This paper is under CC 4.0 license. available on arxiv Authors: (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References THE PROJECT The laboratory Pepper was placed, in the role of mediator and dialogue partner, within the therapeutic laboratory[2] to promote autonomy and functional acquisition in autistic children with low support needs (level 1 support according to DSM-5 [1]). During the laboratory, conducted between the end of February 2021 and the beginning of June 2021, the exchanges, and interactions between autistic children in rehabilitation settings were explored, supported by the robot Pepper aiming to create an environment that would stimulate the development of social and communication skills, as well as strengthen the acquisition of strategies and autonomies related to daily activities, such as snack preparation [14]–[17]. Four autistic children with low support needs (level 1 support), aged 11 to 14 years, participated in the lab. During the lab sessions, the children had the opportunity to interact both directly with the robot and to participate in group activities, guided and coordinated by Pepper itself and the specialized operators, including educators, speech therapists, and psychologists [14]–[17]. The workshop sessions (16 in total), which were held weekly with a duration of two hours each, were recorded via a fixed camera, in addition to Pepper's 2D cameras (forehead camera and tablet camera), to allow post-hoc analysis to be conducted. During the meetings, two trainee students filled out evaluation forms provided by the psychotherapists, noting the progress of the sessions in a diary. These notes were later reworked to make reports, shared within the working group, reflecting the children's evolving behavior during their interactions with the robot [14]–[17]. To ensure the children's privacy, the "PepperForAutism" application was implemented in the robot during data collection, which consists of three main modules (Android app, web platform and backend) integrated into Jumple SRL's Pyramid platform. This system enabled the Pepper robot to uniquely identify the child with whom it was interacting, allowing data to be collected anonymously through association with a random ID known only to authorized individuals [14]–[17]. 1.1. The lab's sessions in details The sessions, lasting about two hours, were conducted by one or two specialized operators (educators, speech therapists, psychologists, etc.) assisted by two or three trainees. These were held in a room furnished with table and chairs and equipped with the tools necessary to carry out the activities, inside an apartment located in the center of Turin, made available by the Paideia Foundation also equipped with a bathroom, living room and kitchen. During the sessions, each child was given the opportunity to interact directly with Pepper and perform the activities planned for the group through the directions provided by the robot, with the help of the specialized operators and in a real context setting to leverage the benefit of a real-world evaluation [23]. Each session was phased and was conducted according to the following structure: 1. Welcome: children were made to sit in the apartment and welcomed by the therapists and the robot who, at the beginning of each session, provided directions on how to prepare to participate in the workshop. 2. First social time: dialogue session with the robot on a pre-established topic of interest (e.g., music, video games, etc.). The conversation is designed to be initialized by both the children and the robot. In the first case, "Getting Acquainted", the children would stand in front of the robot and, after waiting for it to catch their presence, proceed to ask a question of any sort. In the second case, identified as "Making Friends," Pepper would call the children one at a time to suggest a topic of conversation to each, according to a script updated weekly, in the expectation of engaging the other participants in a group conversation. 3. Development of practical skills (snack preparation and/or homework execution): Pepper played the role of activity coordinator, offering snack preparation instructions and explanatory videos displayed on the built-in tablet, in to guide the children in performing the task, with the therapists ready to intervene if needed. Starting from the 11th meeting, this phase was enriched by the inclusion of the activity of doing school homework. The children, who had been previously warned about the need to come equipped with their own school supplies, were asked to consult their journal to select a subject whose homework they wished to complete with the assistance of Pepper, who, after identifying a range of topics, proceeded by providing information on what materials were necessary or optional to proceed effectively with the task they chose. Again, the use of explanatory videos was provided. 4. Second social moment: post snack (or post homework) dialogue. The conversation is guided in the mode ("Moment of Knowledge" or "Making Friends") not explored in the First Social Moment. 5. Final Feedback: Before ending the session with greetings, Pepper asks the children to give their opinions about the meeting. In this phase, children were asked to stand in front of the robot and state their mood, choosing from the illustrated faces on the integrated tablet suitable for representing sadness, neutrality, and happiness. Because the workshop took place during a period characterized by social distancing due to the Covid-19 pandemic, children were required to wear a clear visor as an alternative to the surgical mask so that Pepper could take a picture, which was later used by the trainees to assess the consistency of the emotion indicated by the child with his or her facial expressions and the mood inferred by the robot [16]. 1.2. Welcome At the beginning of each meeting, Pepper welcomed the children and provided them with instructions for getting ready for and participating in the lab. These instructions included descriptive phrases and directives similar to those found in social stories, with the purpose of guiding and supporting the children during the activities. This phase is the one that produced the most noticeably positive results. Although Pepper offered to repeat the welcome directions each time, the children demonstrated that they remembered from one encounter to the next the rules of the lab and were able to correctly follow the instructions to take off their shoes and coats and to sanitize their hands before entering the lab, while still benefiting from the psychologist's help when necessary [17]. Starting therapeutic sessions in this way is both functional for the organizational and managerial purposes of the lab itself, and in line with the natural inclination of autistic minds to seek order and behavioral patterns that facilitate schematization of routines, predictability of events, and, in general, repetition [1]. This choice offers participants a sensitive approach toward the peculiarities of their minds, allowing them to feel immediately at ease, making it easier to introduce the activities planned for the day, and encourages children to be genuine and spontaneous, immediately comforting them by implicitly declaring that they are entering a safe environment, designed specifically to support and stimulate their minds, with no prejudice about their frailties. 1.3. Practical skills training At this stage, the robot is assigned to coordinate the activity of snack preparation, offering snacks with recipes that were from time to time more and more complex, listing ingredients, encouraging children to get organized, providing guidance on the procedures both verbally and using pictures, animations and demonstration videos played on the built-in tablet, and giving children the opportunity to review the steps if necessary. During this activity, provision was made for Pepper to enter Waiting Mode, both to allow the children time to carry out the assignments and the opportunity for the group to spontaneously chat during the meal. On most occasions, the children took advantage of this time to share their suggestion for questions to ask Pepper during the conversational moments, and about activities and topics they would like to cover and proposal to improve the robot’s dialogue structure to make it more friendly and pleasant to talk with. This was also because the time spent with Pepper was perceived as valuable, and the children were inclined to focus on it even when they were not communicating with it directly since they felt empowered with the responsibility and opportunity to contribute to the improvement of the robot. The snack time produced good results and, in addition to being evidently stimulating for the young participants, was also one of their favorite moments; in fact, in most of the encounters they asked to start the lab session with this activity. The children showed appreciation for Pepper's help and, from the very beginning, good levels of autonomy, although the excitement associated with this time often affected the ability to maintain high concentration levels, as indicated by the scores given by the operatives [17]. Beginning in the eleventh meeting, the school homework activity was introduced, in which Pepper offered the children directions on what tools would be needed and tricks for doing the task they chose, always accompanying the verbal instructions with multimedia elements played on the integrated tablet when needed [17]. Regarding this phase, there was not sufficient data collected to assess its effectiveness, but it is still possible to consider the direct feedback from the children, who repeatedly and openly stated that they did not want to «waste» their time in the lab with ordinary activities, suggesting alternative skill-building activities (e.g., chemistry or robotics experiments), while only on one occasion one child did say that he appreciated the time devoted to doing homework, as it allowed him to associate the lab with the environment more familiar to him, of school. 1.4. Conversations During the dialogue moments, the children were directly asked whether they wanted to "get acquainted" or "make friends" with the robot. In the former, they were given the opportunity to ask questions of their interest directly to Pepper, while in the latter, it was Pepper who asked them questions about their interests so as to encourage a collective conversation on the topic. Unfortunately, both modes did not produce satisfactory results, as they were too far removed from the human conversational style [20], consisting of simple repartee, without the possibility of in depth or direct conversation development with the robot, due to the lack of an open conversational system that would allow the robot to use a communicative style more similar to natural language [17]. Giving children the opportunity to ask questions to the robot at a time when it lacked the ability to draw on ontologies to respond but had a dialogue system based on the manual and weekly update of its script, has produced various difficulties during the conversational moments. apart from not taking into account the difficulty of autistic people in initializing conversations [21], has produced several situations of misunderstanding, in which the robot, for technical difficulties (e.g. background noise, speech defects, etc.) or the absence of the input provided by the child within its script, it found itself unable to provide a coherent and satisfactory response [17]. After few interactions, the children began to ask themselves about Pepper with derisive attitude, deliberately asking nonsensical questions attempting to get funny answers or to evaluate how far they could go before sending cause a malfunction. An example of mocking interaction that has produced a lot of hilarity and has been revived during several meetings is related to the understanding by the children that pronounce at the beginning of the question the keywords «have you ever been in...» allowed access to the standard answer: «no, but I would like to visit the whole world», even though the question was not completed with a real location, prompting participants to ask if Pepper had ever visited the Moon, heaven or the land of the stupidity. As per the goal of stimulating the social interaction between the participants, the technical difficulties that emerged during this phase encouraged the children's curiosity about the robot, encouraging collective discussion about how Pepper works and brainstorming suggestions for improvements to make it a more enjoyable conversation partner. On the other hand, the analysis of these phases, despite not having produced satisfactory therapeutic results, is extremely interesting to conduct an evaluation of interactions aimed at improving the dialogue system, pointing out that, even before technical problems, communication with the robot is deficient because it is not fluent in the slang used by children, compromising its position as a mediator because it is unable to act as an interpreter between children and specialized adults working in the laboratory. [2] Ethical approval for this study was obtained from the bioethical committee of the University of Turin, with approval number: 0664572.

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